Detecção de SARS-CoV-2 em águas residuárias da região metropolitana da Grande Vitória (RMGV) com proposta de modelagem numérica populacional para COVID-19

Abstract

After the identification of SARS-CoV-2 and the declaration by the WHO of the COVID19 pandemic, Medema et al (2020) described that, through the analysis of samples of raw wastewater from treatment plants and airports, it was possible to identify the RNA of SARS-COV-2, being the first work to detect that virus in samples of wastewater. Therefore, this study aims to carry out the detection of SARS-CoV-2 in wastewater from the metropolitan region of Grande Vitória (RMGV), in the state of Espírito Santo, in ditches and hospitals and to establish, through a developed computational tool, a proposal for the compilation and dissemination of data based on wastewater-based epidemiology (WBE). Samples of raw wasteeater were collected in ditches and hospitals and concentrated through precipitation by polyethylene glycol. RNA was extracted using magnetic beads and detection was performed through the target genes E and RdRp. In the total of samples evaluated, seven (25.9%) were determined to be positive for the presence of SARS-CoV-2 in ditches with an average Ct of 26 and thirty-two (45.7%) in hospitals with an average Ct of 28 .It was possible to detect SARS-CoV-2 VoCs in 4 samples from ditches, with the gamma variant (P1) being detected. The algorithms used to test the computational tool did not show satisfactory linearity of results, however, they showed the potential of this tool developed for the study of epidemiological evaluation studies. This study is the first to establish the correlation of the detection of SARS-CoV-2 RNA in urban wastewater present in the ditches, with population groups located in the municipality of Vila Velha, Espírito Santo, as well as in hospitals in the RMGV, successfully implementing the proposed methodology. It is recommended the maintenance and development of new studies that seek to define standards for environmental monitoring that can contribute to the advancement and implementation of the computational tool tested here for local approaches and strategies of wastewater-based epidemiology (WBE).